Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
The term “smart grid” is often used to refer technology that utility companies use to monitor and control the delivery and production/consumption of a resource such as electricity, gas, water, etc., using computer-based remote control and automation. The smart grid is empowered by information technology (IT) tools for monitoring and control. The smart grid envisions several sophisticated services, which heavily depend on near real-time monitoring of the assets and functionality they provide. However, we still face several problems when it comes down to assessment of the infrastructure, not to mention estimation of behavior. Today it is very difficult to: (i) anticipate the requirements for all of its layers e.g., of a smart metering deployment needed in hardware and software; and (ii) modify, on the fly, the infrastructure to guarantee envisioned constraints such as performance or quality of service (QoS). The smart grid promises a more versatile and intelligent network of collaborating actors that will eventually lead to better utilization of its resources in order to achieve goals such as energy efficiency. The smart grid is a cyber-physical system (CPS) that depends on IT and has spawned several traditional domains and (business) processes (e.g. industrial automation, smart metering, etc.) in an effort to deliver an optimized critical energy infrastructure and auxiliary services.
As users in the smart grid era will be able to not only consume but also produce energy (referred to as “prosumers”), the dynamics and complexity of the system increases. Information and communication technologies may be employed to provide insight to the prosumer's current and future activities that is not possible in the conventional grid. In the future, devices may no longer be single role devices that either only consume energy (e.g., a home appliance) or only produce energy (e.g., a photovoltaic panel), but rather will have interchangeable dual roles of energy consumer and energy producer, and hence the term “prosumer devices.” A typical example of a prosumer device is the electric car, which consumes electricity when driven, and produces electricity that is stored when braking. A commonly described usage scenario involves a fleet of electric cars. While the cars are being driven or charged, they can be viewed as “consuming” energy. However, if the need arises, they can feed the energy stored in their batteries to the grid as providers.
As energy monitoring and management systems become increasingly integrated with enterprise systems, enterprise services will integrate information coming from highly distributed smart metering points in near real-time, process it, and take appropriate decisions. The decision making process can consider prosumer-specific behavioral information either measured, assumed, or explicitly provided by the prosumer. This will give rise to a new generation of applications that depend on “real-world” services which constantly hold actualized data as they are generated. Furthermore, the integration of potential future behavior of the prosumer may enable better correlation and analytics. Such information is usually not available at all, or in the best case only acquired by local systems (e.g., a building's energy management system), and over dedicated channels and proprietary interfaces that hinder further dissemination of the information.